{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dd498615",
   "metadata": {},
   "source": [
    "# oveview\n",
    "> svm 需要解决以下四个问题,\n",
    "1. 对于分类问题硬边界优化函数的建立、对偶问题的建立、模型的求解,\n",
    "2. 软边界的加入，损失函数以及简单化处理，模型的求解,\n",
    "3. 核函数的加入，模型的求解,\n",
    "4. 对于拟合问题优化函数的建立、对偶问题的建立、模型的求解,\n",
    "\n",
    "> svm 需要理解的几个重要超参数,\n",
    "1. 软边界中的正则化常数$C$ 以及模型超参数中未出现的松弛变量$\\xi$,\n",
    "2. 核函数名称，核函数维度\\n\",\n",
    "3. 对于SVR而言，还要确定边界参数$\\epsilon$,\n",
    "\n",
    "> svm 注意事项,\n",
    "1. 不适合有部分残缺的数据集,\n",
    "2. 过度升维可能会导致过拟合现象,\n",
    "3. 极度依赖数据本身的范围，使用前最好进行标准化处理"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be4e16ff",
   "metadata": {},
   "source": [
    "# hard-margin model\n",
    "## mathmatical background\n",
    "> special analytic geometry\n",
    "$$\n",
    "    d = \\frac{|\\omega^T \\cdot x + b|}{||\\omega||}\n",
    "$$\n",
    "\n",
    "> to make sure each data point are assigned to correct class, label and predict value should be of the same sign\\n\",\n",
    "\n",
    "$$\n",
    "    y_i(\\omega^T \\cdot x_i + b) \\ge 1\n",
    "$$\n",
    "\n",
    "> where 1 here is set as hard-margin, which is for the sake of calculation simplicity\\n\",\n",
    "> here, we assume $\\omega^T \\cdot x + b$ set to be 1, distance between to support vectors should be:\n",
    "\n",
    "$$\n",
    "    \\frac{2}{||\\omega||}\n",
    "$$\n",
    "\n",
    "> then optimization problem is:\n",
    "\n",
    "$$\n",
    "\\begin{array}{ll}\n",
    "\\mathop{\\arg\\max}\\limits_{\\omega}  & \\frac{2}{||\\omega||} \\\\\n",
    "s.t. & y_i(\\omega^T \\cdot x_i + b) \\ge 1 \\space,\\space i = 1,2,\\dots n\n",
    "\\end{array}\n",
    "$$\n",
    "\n",
    "> which could be transferred to\n",
    "\n",
    "$$\n",
    "\\begin{array}{ll}\n",
    "\\mathop{\\arg\\min}\\limits_{\\omega}  & \\frac{1}{2}{||\\omega||}^2 \\\\\n",
    "s.t. & y_i(\\omega^T \\cdot x_i + b) \\ge 1 \\space,\\space i = 1,2,\\dots n,\n",
    "\\end{array}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fea8666f",
   "metadata": {},
   "source": [
    "## mathmatical derivation\n",
    "> Lagrange multiplier method\n",
    "\n",
    "$$\n",
    "    L(\\omega, b, \\alpha) = \\frac{1}{2}{||\\omega||}^2 + \\sum_{i=1}^{n}{\\alpha_i[1-y_i(\\omega^T \\cdot x + b)]}\n",
    "$$\n",
    "\n",
    "> kkt condition:\n",
    "\n",
    "$$\n",
    "\\left\\{\n",
    "\\begin{array}{ll}\n",
    "\\alpha_i & \\\\ge & 0 \\\\\n",
    "y_i(\\omega^T \\cdot x + b) - 1 & \\ge & 0 \\\\\n",
    "\\alpha_i \\cdot [y_i(\\omega^T \\cdot x + b) - 1] & = & 0\n",
    "\\end{array}\n",
    "\\right .\n",
    "$$\n",
    "\n",
    "> duel problem, note that the upper bound of the optimization result of duel problem is the lower bound of the original problem\n",
    "\n",
    "$$\n",
    "\\begin{array}{ll}\n",
    "\\mathop{\\arg\\max}\\limits_{\\alpha}  & \\displaystyle\\sum_{i}^{n}{\\\\alpha_i} - \\frac{1}{2} \\displaystyle\\sum_{i}^{n}\\displaystyle\\sum_{j}^{n}{\\alpha_i\\alpha_jy_iy_j{x_i}^Tx_j} \\\\\n",
    "s.t. & \\displaystyle\\sum_{i}^{n}{\\alpha_iy_i} = 0,\\space i = 1,2,\\dots n \\\\\n",
    "& \\alpha_i \\ge 0,\\space i = 1,2,\\dots n \\\\\n",
    "\\end{array}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d90126ad",
   "metadata": {},
   "source": [
    "# soft-margin model\n",
    "> adding penalty term punishing data points that are off-the-street of the right side of classification plane\n",
    "> note that penalty term contains two main parts: \n",
    "1. regularization constant, in svm case this parameter is $C$, for larger $C$, soft-margin model will perform like one hard-margin model\n",
    "2. cost function, of which independent variable is set to be some index of model, in svm case is the distance $y_i(\\omega^T \\cdot x_i + b)$\n",
    "\n",
    "$$\n",
    "\\begin{array}{ll}\n",
    "\\mathop{\\arg\\min}\\limits_{\\omega} \\frac{1}{2}{||\\omega||}^2 + C \\cdot \\displaystyle\\sum_{i}^{n}{l_{1/0}[1-y_i(\\omega^T \\cdot x_i + b)]}\n",
    "\\end{array}\n",
    "$$\n",
    "\n",
    "> where $l_{1/0}$ is one cost function, this function is related to parameter $\\omega$ and $b$. for the sake of calculation simplicity, we introduce slack variables in replace of cost function, which could be \n",
    "\n",
    "$$\n",
    "\\begin{array}{ll}\n",
    "\\mathop{\\arg\\min}\\limits_{\\omega,\\xi_i} & \\frac{1}{2}{||\\omega||}^2 + C \\cdot \\displaystyle\\sum_{i}^{n}{\\xi_i} \\\\\n",
    "s.t. & y_i[(\\omega^T \\cdot x_i + b)] \\ge 1- \\xi_i\\\\\n",
    "& \\xi_i \\ge 0 \\space , \\space i = 1,2, \\dots, n\\\\\n",
    "\\end{array}\n",
    "$$\n",
    "\n",
    "> where $\\xi_i$ could possibily be greater than 1. in this case, data point would cross the classification plane and locate on the other side of plane. comparing to hard-margin restriction condition $y_i[(\\omega^T \\cdot x_i + b)] \\ge 1$, this condition $y_i[(\\omega^T \\cdot x_i + b)] \\ge 1- \\xi_i$ takes far-off-street points into consideratioin\n",
    "\n",
    "> in this case, $\\xi_i$ is the replacement of cost function, note that cost function is also the function of $\\omega$ thus equation simplification as well as duel problem making should take $\\xi_i$ into consideration\n",
    "\n",
    "> lagrange multiplier method:\n",
    "\n",
    "$$\n",
    "   L(\\omega, b, \\alpha, \\xi, \\mu) = \\frac{1}{2}{||\\omega||}^2 + \\sum_{i=1}^{n}{\\alpha_i[1 - \\xi_i - y_i(\\omega^T \\cdot x + b)]} - \\displaystyle\\sum_{i=1}^{n}{\\mu_i\\xi_i}\n",
    "$$\n",
    "\n",
    "> corresponding kkt condition:\n",
    "\n",
    "$$\n",
    "\\left \\{\n",
    "\\begin{array}{ll}\n",
    "\\alpha_i & \\ge & 0 \\\\\n",
    "y_i[(\\omega^T \\cdot x_i + b)] - 1 + \\xi_i & \\ge & 0 \\\\\n",
    "\\alpha_i \\cdot [y_i(\\omega^T \\cdot x_i + b) - 1 + \\xi_i] & = & 0 \\\\\n",
    "\\xi_i & \\ge & 0 \\\\\n",
    "\\mu_i & \\ge & 0 \\\\\n",
    "\\mu_i\\xi_i & = & 0\n",
    "\\end{array}\n",
    "\\right .\n",
    "$$\n",
    "\n",
    "> dual problem is like:\n",
    "\n",
    "$$\\\n",
    "\\begin{array}{ll}\n",
    "\\mathop{\\arg\\max}\\limits_{\\alpha}  & \\displaystyle\\sum_{i}^{n}{\\alpha_i} - \\frac{1}{2} \\displaystyle\\sum_{i}^{n}\\displaystyle\\sum_{j}^{n}{\\alpha_i\\alpha_jy_iy_j{x_i}^Tx_j} \\\\\n",
    "s.t. & \\displaystyle\\sum_{i}^{n}{\\alpha_iy_i} = 0,\\space i = 1,2,\\dots n \\\\\\\n",
    "& 0 \\le \\alpha_i \\le C ,\\space i = 1,2,\\dots n \\\\\n",
    "\\end{array}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "851e4e72",
   "metadata": {},
   "source": [
    "## linear svc\n",
    "1. LinearSVC is a soft-margin model\n",
    "2. two hyperparameters should be taken into consideration:\n",
    "    > C - penalty factor <br>\n",
    "    > loss - loss function <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "786157a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('linear_svc', LinearSVC(C=1, loss='hinge'))])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "iris_raw = load_iris()\n",
    "# petal length, petal width\n",
    "X_iris = iris_raw[\"data\"][:,(2,3)]\n",
    "# Iris-virginica\n",
    "y_iris = (iris_raw['target'] == 2).astype(np.float64)\n",
    "\n",
    "svm_clf = Pipeline(\n",
    "        ((\"scaler\",StandardScaler()),\n",
    "         (\"linear_svc\", LinearSVC(C=1, loss=\"hinge\"))\n",
    "))\n",
    "svm_clf.fit(X_iris,y_iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f14dfc23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.predict([[5.5, 1.7]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1d67736b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize=(6,4),\n",
    "                      facecolor='whitesmoke',\n",
    "                      edgecolor='gray')\n",
    "x = np.linspace(min(X_iris[:,0]-0.5),max(X_iris[:,0])+0.5,100)\n",
    "y = np.linspace(min(X_iris[:,1]-0.5),max(X_iris[:,1])+0.5,100)\n",
    "xx, yy = np.meshgrid(x,y)\n",
    "zz = np.zeros_like(xx)\n",
    "for xi in range(len(xx)):\n",
    "    for yi in range(len(yy)):\n",
    "        zz[xi,yi] = svm_clf.predict([[xx[xi,yi],yy[xi,yi]]])\n",
    "\n",
    "X_1 = X_iris[y_iris.astype(np.bool8)] \n",
    "X_0 = X_iris[~y_iris.astype(np.bool8)]\n",
    "        \n",
    "ax.contourf(xx,yy,zz,cmap=plt.cm.PiYG)\n",
    "ax.scatter(X_0[:,0],X_0[:,1],marker=\">\",color=\"orange\",lw=.5,label=\"non-virginica\")\n",
    "ax.scatter(X_1[:,0],X_1[:,1],marker=\">\",color=\"blue\",lw=.5,label=\"virginica\")\n",
    "ax.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fc4d4f1",
   "metadata": {},
   "source": [
    "## non-linear svc\n",
    "1. in this case, we use PolynomialFeatures to \"proliferate\" input features, this means making input data a higher dimensional vector, hyperplane for svm is already set in the beginning phase, no need for kernel transformation\n",
    "\n",
    "2. important hyperparameters are:\n",
    "    > kernel <br>\n",
    "    > degree <br>\n",
    "    > C <br>\n",
    "    > coef0 and gamma are for 'ploy' 'sigmoid' and 'rbf' kernel <br>\n",
    "    > decision_function_shape: 'ovo' or 'ovr'(default) <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6aec4b06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('poly_features', PolynomialFeatures(degree=3)),\n",
       "                ('scaler', StandardScaler()),\n",
       "                ('svm_clf', LinearSVC(C=10, loss='hinge', max_iter=10000))])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "X_moon1, y_moon1 = make_moons(n_samples=100, noise=.5)\n",
    "polynomial_svm_clf = Pipeline((\n",
    "        (\"poly_features\",PolynomialFeatures(degree=3)),\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", LinearSVC(C=10, loss=\"hinge\",max_iter=10000))\n",
    "))\n",
    "\n",
    "polynomial_svm_clf.fit(X_moon1,y_moon1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bea1c364",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbdabec2040>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mlp\n",
    "from collections import Counter\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "x1 = np.linspace(min(X_moon1[:,0])-0.5,max(X_moon1[:,0])+0.5, 100)\n",
    "y1 = np.linspace(min(X_moon1[:,1])-0.5,max(X_moon1[:,1])+0.5, 100)\n",
    "\n",
    "xx1, yy1 = np.meshgrid(x1,y1)\n",
    "zz1 = np.zeros_like(xx1)\n",
    "for idx in range(len(xx1)):\n",
    "    for idy in range(len(yy1)):\n",
    "        zz1[idx,idy] = polynomial_svm_clf.predict([[xx1[idx,idy],yy1[idx,idy]]])\n",
    "\n",
    "X_moon_11 = X_moon1[y_moon1.astype(np.bool8)]\n",
    "X_moon_10 = X_moon1[~y_moon1.astype(np.bool8)]\n",
    "\n",
    "fig, ax = plt.subplots(1,1,figsize=(6,4),\n",
    "                      facecolor=\"whitesmoke\",\n",
    "                      edgecolor=\"gray\")\n",
    "ax.contourf(xx1,yy1,zz1,cmap=plt.cm.PiYG)\n",
    "ax.scatter(X_moon_11[:,0],X_moon_11[:,1],marker=\">\",color=\"blue\",lw=.5,label=\"class-1\")\n",
    "ax.scatter(X_moon_10[:,0],X_moon_10[:,1],marker=\">\",color=\"orange\",lw=.5,label=\"class-0\")\n",
    "ax.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6adfd92b",
   "metadata": {},
   "source": [
    "## kernel svc\n",
    "1. in this case dimensional transformation takes place in optimization problem solving process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b5af9d62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('svm_clf', SVC(C=10, coef0=0.1, kernel='poly'))])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "X_moon2, y_moon2 = make_moons(n_samples=100, noise=.75)\n",
    "\n",
    "kernel_svm_clf = Pipeline((\n",
    "    (\"scaler\",StandardScaler()),\n",
    "    (\"svm_clf\",SVC(C=10, kernel=\"poly\",degree=3, coef0=0.1, gamma=\"scale\"))\n",
    "))\n",
    "kernel_svm_clf.fit(X_moon2,y_moon2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0379038f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x2 = np.linspace(min(X_moon2[:,0])-0.5,max(X_moon2[:,0])+0.5, 100)\n",
    "y2 = np.linspace(min(X_moon2[:,1])-0.5,max(X_moon2[:,1])+0.5, 100)\n",
    "\n",
    "xx2, yy2 = np.meshgrid(x2,y2)\n",
    "zz2 = np.zeros_like(xx2)\n",
    "for idx in range(len(xx2)):\n",
    "    for idy in range(len(yy2)):\n",
    "        zz2[idx,idy] = kernel_svm_clf.predict([[xx2[idx,idy],yy2[idx,idy]]])\n",
    "\n",
    "X_moon_21 = X_moon2[y_moon2.astype(np.bool8)]\n",
    "X_moon_20 = X_moon2[~y_moon2.astype(np.bool8)]\n",
    "\n",
    "fig, ax = plt.subplots(1,1,figsize=(6,4),\n",
    "                      facecolor=\"whitesmoke\",\n",
    "                      edgecolor=\"gray\")\n",
    "ax.contourf(xx2,yy2,zz2,cmap=plt.cm.PiYG)\n",
    "ax.scatter(X_moon_21[:,0],X_moon_21[:,1],marker=\">\",color=\"blue\",lw=.5,label=\"class-1\")\n",
    "ax.scatter(X_moon_20[:,0],X_moon_20[:,1],marker=\">\",color=\"orange\",lw=.5,label=\"class-0\")\n",
    "ax.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64c72898",
   "metadata": {},
   "source": [
    "# kernel function\n",
    "> raise dimensionality, yet the inner product result of a high dimensional space can be represented by a vector of the original space\n",
    "> kernal function are delicately designed to meet the aforementioned condition\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6cc64b9",
   "metadata": {},
   "source": [
    "## tweaking hyperparameters\n",
    "1. 4 parameters are important for kernel tranformation\n",
    "    > kernel : choose from sigmoid, linear, poly, rbf, rbf is most recommended <br>\n",
    "    > coef0 : weight on higher power terms <br>\n",
    "    > degree: works for poly, target dimensions <br>\n",
    "    > gamma : works for rbf, calculate gamma <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "abe5585e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=SVC(),\n",
       "             param_grid=[{'C': [5, 10, 20], 'coef0': [0.2, 0.5, 0.8, 1],\n",
       "                          'degree': [3, 5, 8, 10], 'kernel': ['poly']},\n",
       "                         {'C': [1000, 10, 0.01],\n",
       "                          'gamma': [5, 1, 0.1, 0.01, 0.001],\n",
       "                          'kernel': ['rbf']}],\n",
       "             scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.datasets import make_moons\n",
    "\n",
    "X_grid, y_grid = make_moons(n_samples=100, noise=.2)\n",
    "grid_svc = SVC()\n",
    "svm_param_grid=[\n",
    "    {\"C\":[5,10,20],\"degree\":[3,5,8,10],\"coef0\":[.2,.5,.8,1],\"kernel\":[\"poly\"]},\n",
    "    {\"C\":[1000,10,0.01],\"gamma\":[5,1,0.1,0.01,0.001],\"kernel\":[\"rbf\"]},\n",
    "]\n",
    "svc_grid_search = GridSearchCV(grid_svc,svm_param_grid,\n",
    "                              cv=5,scoring=\"neg_mean_squared_error\")\n",
    "svc_grid_search.fit(X_grid, y_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ef0e87bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=5, coef0=0.5, degree=8, kernel='poly')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c280cd7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def svm_subplot(model, X, y, xx, yy, ax, x_pos, y_pos, title):\n",
    "    '''\n",
    "    @parameters\n",
    "    -------------------\n",
    "    model: pre-trained model \n",
    "    X: raw data, in this case is X_grid (100,2)\n",
    "    y: raw target, in this case is y_grid (100,)\n",
    "    xx: meshgrid x\n",
    "    yy: meshgrid y\n",
    "    ax: axis instance built from plt.subplots()\n",
    "    x_pos: row index of ax\n",
    "    y_pos: column index of ax\n",
    "    title: title for this ax\n",
    "    '''\n",
    "    X_1 = X[y.astype(np.bool8)]\n",
    "    X_0 = X[~y.astype(np.bool8)]\n",
    "    zz = np.zeros_like(xx)\n",
    "    for idx in range(len(xx)):\n",
    "        for idy in range(len(yy)):\n",
    "            zz[idx,idy] = model.predict([[xx[idx,idy],yy[idx,idy]]])\n",
    "    ax[x_pos,y_pos].contourf(xx,yy,zz,cmap=plt.cm.PiYG)\n",
    "    ax[x_pos,y_pos].scatter(X_1[:,0],X_1[:,1],marker=\">\",color=\"blue\",lw=.5,label=\"class-1\")\n",
    "    ax[x_pos,y_pos].scatter(X_0[:,0],X_0[:,1],marker=\">\",color=\"orange\",lw=.5,label=\"class-0\")\n",
    "    ax[x_pos,y_pos].legend(loc=\"best\")\n",
    "    ax[x_pos,y_pos].set_title(title)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4512d61e",
   "metadata": {},
   "source": [
    "## polynomial kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7952837b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.datasets import make_moons\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "X_grid, y_grid = make_moons(n_samples=100, noise=.2)\n",
    "\n",
    "x = np.linspace(min(X_grid[:,0])-0.5,max(X_grid[:,0])+0.5, 100)\n",
    "y = np.linspace(min(X_grid[:,1])-0.5,max(X_grid[:,1])+0.5, 100)\n",
    "xx, yy = np.meshgrid(x,y)\n",
    "\n",
    "fig, ax = plt.subplots(2,2,figsize=(10,8),\n",
    "                      facecolor=\"whitesmoke\",\n",
    "                      edgecolor=\"gray\")\n",
    "# degree 3\n",
    "kernel_svm_clf_1 = Pipeline((\n",
    "    (\"scaler\",StandardScaler()),\n",
    "    (\"svm_clf\",SVC(C=5, kernel=\"poly\",degree=3, coef0=0.5))\n",
    "))\n",
    "kernel_svm_clf_1.fit(X_grid,y_grid)\n",
    "svm_subplot(kernel_svm_clf_1,X_grid,y_grid,xx,yy,ax,\n",
    "            x_pos=0,y_pos=0,title=\"C=5, degree=3, coef=0.5\")\n",
    "# degree 5\n",
    "kernel_svm_clf_2 = Pipeline((\n",
    "    (\"scaler\",StandardScaler()),\n",
    "    (\"svm_clf\",SVC(C=5, kernel=\"poly\",degree=5, coef0=0.5))\n",
    "))\n",
    "kernel_svm_clf_2.fit(X_grid,y_grid)\n",
    "svm_subplot(kernel_svm_clf_2,X_grid,y_grid,xx,yy,ax,\n",
    "            x_pos=0,y_pos=1,title=\"C=5, degree=5, coef=0.5\")\n",
    "# degree 8\n",
    "kernel_svm_clf_3 = Pipeline((\n",
    "    (\"scaler\",StandardScaler()),\n",
    "    (\"svm_clf\",SVC(C=5, kernel=\"poly\",degree=8, coef0=0.5))\n",
    "))\n",
    "kernel_svm_clf_3.fit(X_grid,y_grid)\n",
    "svm_subplot(kernel_svm_clf_3,X_grid,y_grid,xx,yy,ax,\n",
    "            x_pos=1,y_pos=0,title=\"C=5, degree=8, coef=0.5\")\n",
    "# degree 10\n",
    "kernel_svm_clf_4 = Pipeline((\n",
    "    (\"scaler\",StandardScaler()),\n",
    "    (\"svm_clf\",SVC(C=5, kernel=\"poly\",degree=10, coef0=0.5))\n",
    "))\n",
    "kernel_svm_clf_4.fit(X_grid,y_grid)\n",
    "svm_subplot(kernel_svm_clf_4,X_grid,y_grid,xx,yy,ax,\n",
    "            x_pos=1,y_pos=1,title=\"C=5, degree=10, coef=0.5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1324a82a",
   "metadata": {},
   "source": [
    "## gaussian rbf kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "19783fb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.datasets import make_moons\n",
    "from sklearn.svm import SVC\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "X_grid2, y_grid2 = make_moons(n_samples=100, noise=.5)\n",
    "\n",
    "x = np.linspace(min(X_grid2[:,0])-0.5,max(X_grid2[:,0])+0.5, 100)\n",
    "y = np.linspace(min(X_grid2[:,1])-0.5,max(X_grid2[:,1])+0.5, 100)\n",
    "xx, yy = np.meshgrid(x,y)\n",
    "\n",
    "fig, ax = plt.subplots(2,2,figsize=(10,8),\n",
    "                      facecolor=\"whitesmoke\",\n",
    "                      edgecolor=\"gray\")\n",
    "# C=0.001, gamma=0.1\n",
    "rbf_svm_clf_1 = Pipeline((\n",
    "    (\"scaler\",StandardScaler()),\n",
    "    (\"svm_clf\",SVC(C=0.001, kernel=\"rbf\",gamma=0.1))\n",
    "))\n",
    "rbf_svm_clf_1.fit(X_grid2,y_grid2)\n",
    "svm_subplot(rbf_svm_clf_1,X_grid2,y_grid2,xx,yy,ax,\n",
    "            x_pos=0,y_pos=0,title=\"C=0.001, gamma=0.1\")\n",
    "# C=0.001, gamma=5\n",
    "rbf_svm_clf_2 = Pipeline((\n",
    "    (\"scaler\",StandardScaler()),\n",
    "    (\"svm_clf\",SVC(C=0.001, kernel=\"rbf\",gamma=5))\n",
    "))\n",
    "rbf_svm_clf_2.fit(X_grid2,y_grid2)\n",
    "svm_subplot(rbf_svm_clf_2,X_grid2,y_grid2,xx,yy,ax,\n",
    "            x_pos=0,y_pos=1,title=\"C=0.001, gamma=5\")\n",
    "# C=1000, gamma=0.1\n",
    "rbf_svm_clf_3 = Pipeline((\n",
    "    (\"scaler\",StandardScaler()),\n",
    "    (\"svm_clf\",SVC(C=1000, kernel=\"rbf\",gamma=0.1))\n",
    "))\n",
    "rbf_svm_clf_3.fit(X_grid2,y_grid2)\n",
    "svm_subplot(rbf_svm_clf_3,X_grid2,y_grid2,xx,yy,ax,\n",
    "            x_pos=1,y_pos=0,title=\"C=1000, gamma=0.1\")\n",
    "# C=1000, gamma=5\n",
    "rbf_svm_clf_4 = Pipeline((\n",
    "    (\"scaler\",StandardScaler()),\n",
    "    (\"svm_clf\",SVC(C=1000, kernel=\"rbf\",gamma=5))\n",
    "))\n",
    "rbf_svm_clf_4.fit(X_grid2,y_grid2)\n",
    "svm_subplot(rbf_svm_clf_4,X_grid2,y_grid2,xx,yy,ax,\n",
    "            x_pos=1,y_pos=1,title=\"C=1000, gamma=5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3943af4a",
   "metadata": {},
   "source": [
    "# SVR\n",
    "> different from SVC-SVM, regression only takes predictions that are largely diviating from target into consideration when calculating cost function(optimization problem). This margin is set as $\\epsilon$\n",
    "> thus, we take this cost function into practice\\\n",
    "\n",
    "$$\n",
    "    l_{\\epsilon}(z) = \n",
    "\\left\\{\n",
    "\\begin{array}{ll}\n",
    "0& , & if |z| \\le \\epsilon \\\\\n",
    "|z| - \\epsilon& , & otherwise\n",
    "\\end{array}\n",
    "\\right .\n",
    "$$\n",
    "\n",
    "> then optimization problem becomes:\n",
    "\n",
    "$$\n",
    "    \\mathop{\\arg\\min}\\limits_{\\omega} \\frac{1}{2}{||\\omega||}^2 + C \\cdot \\displaystyle\\sum_{i}^{n}{l_{\\epsilon}[(\\omega^T \\cdot x_i + b)-y_i]}\n",
    "$$\n",
    "\n",
    "> introduce slack variable into model:\n",
    "\n",
    "$$\n",
    "\\begin{array}{ll}\n",
    "\\mathop{\\arg\\min}\\limits_{\\omega,\\xi_i, \\hat\\xi_i} & \\frac{1}{2}{||\\omega||}^2 + C \\cdot \\displaystyle\\sum_{i}^{n}{\\xi_i + \\hat\\xi_i} \\\\\n",
    "s.t.  \n",
    "      & (\\omega^T \\cdot x_i + b)-y_i \\le \\epsilon + \\xi_i \\space,\\\\\n",
    "      & y_i - (\\omega^T \\cdot x_i + b) \\le \\epsilon + \\hat\\xi_i \\space,\\\\\n",
    "      & \\xi_i \\ge 0 \\space,\\\\\n",
    "      &\\hat\\xi_i \\ge 0 \\space, i= 1,|2,\\dots n\\\\\n",
    "\\end{array}\n",
    "$$\n",
    "\n",
    "> in this case, we just consider $l_{\\epsilon}[(\\omega^T \\cdot x_i + b)-y_i]$ as two inequality constrains\n",
    "> thus duel problem is like:\n",
    "\n",
    "$$\n",
    "\\begin{array}{ll}\n",
    "     \\mathop{\\arg\\max}\\limits_{\\alpha,\\hat\\alpha} & \\sum_{i=1}^{n}{y_i(\\hat\\alpha_i - \\alpha_i)-\\epsilon(\\hat\\alpha_i - \\alpha_i)} - \\frac{1}{2} \\sum_{i=1}^{n}\\sum_{j=1}^{n}{(\\hat\\alpha_i - \\alpha_i)(\\hat\\alpha_j - \\alpha_j){x_i}^Tx_j} \\\\\n",
    "    s.t. & \\sum_{i=1}^{n}{(\\hat\\alpha_i - \\alpha_i)=0} \\\\\n",
    "    & 0 \\le \\alpha_i , \\hat\\alpha_i \\le C\n",
    "\\end{array}\n",
    "$$ \n",
    "\n",
    "> the corresponding kkt conditions are:\n",
    "\n",
    "$$\n",
    "\\left \\{\n",
    "\\begin{array}{ll}\n",
    "    & \\alpha_i(\\omega^T \\cdot x_i + b -y_i - \\epsilon - \\xi_i) = 0 \\\\\n",
    "    & \\hat\\alpha_i(\\omega^T \\cdot x_i + b -y_i - \\epsilon - \\hat\\xi_i) = 0 \\\\\n",
    "    & \\alpha_i \\hat\\alpha_i = 0 \\\\\n",
    "    & \\xi_i\\hat\\xi_i = 0 \\\\\n",
    "    & (C - \\alpha_i)\\xi_i = 0 \\\\\n",
    "    & (C - \\hat\\alpha_i)\\hat\\xi_i = 0 \\\\\n",
    "\\end{array}\n",
    "\\right .\n",
    "$$\n",
    "\n",
    "> as results, we could calculate each $\\mathbf{\\omega}$ with $\\alpha, \\hat\\alpha$:\n",
    "\n",
    "$$\n",
    "    \\mathbf{\\omega} = \\sum_{i=1}^{n}{(\\hat\\alpha_i - \\alpha_i)\\mathbf{x_i}}\n",
    "$$\n",
    "\n",
    "> for scalar $b$, we could find several $\\mathbf{x}$, then calculate the mean value for $b$ according to this formula:\n",
    "\n",
    "$$\n",
    "    b = y_i + \\epsilon - \\sum_{j=1}^{n}{(\\hat\\alpha_j - \\alpha_j)\\mathbf{x_j}^T \\mathbf{x_i}}\n",
    "$$\n",
    "\n",
    "> * where $\\mathbf{x_j}$ is the data points who meet $0<\\alpha_i<C$ condition, and $\\mathbf{x_i}$ are a ramdom data point"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11ccf97c",
   "metadata": {},
   "source": [
    "## linear svr\n",
    "1. for svr, there's only one additional hyperparameter: $\\epsilon$\n",
    "2. two different SVR model\n",
    "    > LinearSVR - only takes epsilon hyperparameter, could customize loss function <br>\n",
    "    > SVR - takes more hyperparameters, could apply kernel trick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7d398a23",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "X_svr = np.linspace(-50,50,100)\n",
    "y_svr = 0.2 * np.power(X_svr,3) + 0.7 * np.power(X_svr,2) - 1.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "edcdad63",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_svc(model, X, y, ax, x_pos, y_pos, title):\n",
    "    '''\n",
    "    @Parameters\n",
    "    ----------------------------------\n",
    "    model: svc model\n",
    "    X: raw data\n",
    "    y: raw target\n",
    "    ax: axis instance of plt\n",
    "    x_pos: row position in axis\n",
    "    y_pos: column position in axis\n",
    "    title: title of axis\n",
    "    '''\n",
    "    X_range = np.linspace(min(X)-0.5, max(X)+0.5, 110)\n",
    "    y_range = model.predict(X_range.reshape(-1,1))\n",
    "    ax[x_pos,y_pos].scatter(X, y, lw=.1, marker=\".\", color='blue',label='raw data')\n",
    "    ax[x_pos,y_pos].plot(X_range, y_range, ls= '-', lw=1, color=\"red\", label=\"svr regression\")\n",
    "    ax[x_pos,y_pos].set_title(title)\n",
    "    ax[x_pos,y_pos].legend(loc='best')\n",
    "    ax[x_pos,y_pos].grid(alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ccb64b20",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 800x1600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(4,2,figsize=(8,16),\n",
    "                     facecolor='whitesmoke',\n",
    "                     edgecolor='gray')\n",
    "plt.subplots_adjust(left=0.09, bottom=0.20, \n",
    "                    right=0.94, top=0.90, \n",
    "                    wspace=.2, hspace=.45)\n",
    "# SVR wit polynomial kernel\n",
    "##########################################\n",
    "# degree=3\n",
    "linsvr_reg_1= Pipeline((\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svr_regressor\", SVR(C=10,degree=3,coef0=0.1,epsilon=0.1,kernel=\"poly\"))\n",
    "))\n",
    "linsvr_reg_1.fit(X_svr.reshape(-1,1), y_svr)\n",
    "plot_svc(linsvr_reg_1,X_svr,y_svr,ax,\n",
    "        x_pos=0, y_pos=0, title=r\"C=10, degree=3, $\\epsilon=0.1$\")\n",
    "# degree=5\n",
    "linsvr_reg_2= Pipeline((\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svr_regressor\", SVR(C=10,degree=5,coef0=0.1,epsilon=0.1,kernel=\"poly\"))\n",
    "))\n",
    "linsvr_reg_2.fit(X_svr.reshape(-1,1), y_svr)\n",
    "plot_svc(linsvr_reg_2,X_svr,y_svr,ax,\n",
    "        x_pos=1, y_pos=0, title=r\"C=10, degree=5, $\\epsilon=0.1$\")\n",
    "# degree=8\n",
    "linsvr_reg_3= Pipeline((\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svr_regressor\", SVR(C=10,degree=8,coef0=0.1,epsilon=0.1,kernel=\"poly\"))\n",
    "))\n",
    "linsvr_reg_3.fit(X_svr.reshape(-1,1), y_svr)\n",
    "plot_svc(linsvr_reg_3,X_svr,y_svr,ax,\n",
    "        x_pos=2, y_pos=0, title=r\"C=10, degree=8, $\\epsilon=0.1$\")\n",
    "# degree=10\n",
    "linsvr_reg_4= Pipeline((\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svr_regressor\", SVR(C=10,degree=10,coef0=0.1,epsilon=0.1,kernel=\"poly\"))\n",
    "))\n",
    "linsvr_reg_4.fit(X_svr.reshape(-1,1), y_svr)\n",
    "plot_svc(linsvr_reg_4,X_svr,y_svr,ax,\n",
    "        x_pos=3, y_pos=0, title=r\"C=10, degree=10, $\\epsilon=0.1$\")\n",
    "# SVR wit Gaussian rbf kernel\n",
    "##########################################\n",
    "# C=100, gamma=5\n",
    "rbfsvr_reg_1= Pipeline((\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svr_regressor\", SVR(C=100,gamma=5,epsilon=0.1,kernel=\"rbf\"))\n",
    "))\n",
    "rbfsvr_reg_1.fit(X_svr.reshape(-1,1), y_svr)\n",
    "plot_svc(rbfsvr_reg_1,X_svr,y_svr,ax,\n",
    "        x_pos=0, y_pos=1, title=r\"C=100, $\\gamma$=5, $\\epsilon$=0.1\")\n",
    "# C=100, gamma=50\n",
    "rbfsvr_reg_2= Pipeline((\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svr_regressor\", SVR(C=100,gamma=50,epsilon=0.1,kernel=\"rbf\"))\n",
    "))\n",
    "rbfsvr_reg_2.fit(X_svr.reshape(-1,1), y_svr)\n",
    "plot_svc(rbfsvr_reg_2,X_svr,y_svr,ax,\n",
    "        x_pos=1, y_pos=1, title=r\"C=100, $\\gamma$=50, $\\epsilon$=0.1\")\n",
    "# C=0.1, gamma=5\n",
    "rbfsvr_reg_3= Pipeline((\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svr_regressor\", SVR(C=0.1,gamma=5,epsilon=0.1,kernel=\"rbf\"))\n",
    "))\n",
    "rbfsvr_reg_3.fit(X_svr.reshape(-1,1), y_svr)\n",
    "plot_svc(rbfsvr_reg_3,X_svr,y_svr,ax,\n",
    "        x_pos=2, y_pos=1, title=r\"C=0.1, $\\gamma$=5, $\\epsilon$=0.1\")\n",
    "# C=0.1, gamma=50\n",
    "rbfsvr_reg_4= Pipeline((\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svr_regressor\", SVR(C=0.1,gamma=50,epsilon=0.1,kernel=\"rbf\"))\n",
    "))\n",
    "rbfsvr_reg_4.fit(X_svr.reshape(-1,1), y_svr)\n",
    "plot_svc(rbfsvr_reg_4,X_svr,y_svr,ax,\n",
    "        x_pos=3, y_pos=1, title=r\"C=0.1, $\\gamma$=50, $\\epsilon$=0.1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2b166f3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/collinsliu/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 100 is different from 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/zh/fmxxp3g1653ccyjmyp5z3bc00000gn/T/ipykernel_37993/836674407.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0mmm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_svr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_svr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_svr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_svr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m plot_svc(svr_reg_1,X_svr,y_svr,ax,\n\u001b[1;32m     25\u001b[0m         x_pos=0, y_pos=0, title=r\"C=10, $\\epsilon$=0.1\")\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    360\u001b[0m             \u001b[0mReturns\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    361\u001b[0m         \"\"\"\n\u001b[0;32m--> 362\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    363\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    364\u001b[0m     \u001b[0m_preprocess_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstaticmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_preprocess_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_base.py\u001b[0m in \u001b[0;36m_decision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    344\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    345\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"csc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"coo\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 346\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msafe_sparse_dot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdense_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintercept_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    347\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    348\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/extmath.py\u001b[0m in \u001b[0;36msafe_sparse_dot\u001b[0;34m(a, b, dense_output)\u001b[0m\n\u001b[1;32m    151\u001b[0m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m     if (\n",
      "\u001b[0;31mValueError\u001b[0m: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 100 is different from 1)"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "fig, ax = plt.subplots(1,2,figsize=(8,6),\n",
    "                     facecolor='whitesmoke',\n",
    "                     edgecolor='gray')\n",
    "plt.subplots_adjust(left=0.09, bottom=0.20, \n",
    "                    right=0.94, top=0.90, \n",
    "                    wspace=.2, hspace=.2)\n",
    "svr_reg_1 = Pipeline((\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svr_regressor\", LinearSVR(C=10,epsilon=0.1,loss=\"hinge\"))\n",
    "))\n",
    "svr_reg_1.fit(X_svr.reshape(-1,1), y_svr)\n",
    "plot_svc(svr_reg_1,X_svr,y_svr,ax,\n",
    "        x_pos=0, y_pos=0, title=r\"C=10, $\\epsilon$=0.1\")\n",
    "\n",
    "svr_reg_2 = Pipeline((\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svr_regressor\", LinearSVR(C=10,epsilon=1,loss=\"hinge\"))\n",
    "))\n",
    "svr_reg_2.fit(X_svr.reshape(-1,1), y_svr)\n",
    "plot_svc(svr_reg_2,X_svr,y_svr,ax,\n",
    "        x_pos=0, y_pos=1, title=r\"C=10, $\\epsilon$=1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e491a1f5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
